This paper proposes a distributed relative-position-based cooperative pointing control scheme for stationary planar multi-agent systems toward a stationary target under switching topologies. First, the proposed scheme only requires the leaders' orientation angles and relative position measurements, without relying on the agents' states in a common global coordinate frame. Then, the cooperative control problem is decoupled into cooperative target localization and local pointing control. In the proposed scheme, the cooperative target localization problem is modeled as a distributed relative position estimation system, in which the system dynamics are determined by the switching topologies and the projection matrices induced by the leaders' orientation angles. Under minimal information conditions, sufficient conditions are established to ensure the convergence of the proposed scheme by developing a novel error estimator and multiple Lyapunov functions. As a result, the generated reference orientations converge to the desired ones, and the local pointing controllers track them asymptotically. Finally, numerical simulations validate the effectiveness of the proposed method.

Full waveform inversion (FWI) is a powerful technique for reconstructing subsurface physical parameters. However, conventional FWI often suffers from cycle-skipping and local-minimum issues. With the rapid development of deep learning, data-driven methods have attracted increasing attention in FWI, where the inversion problem is reformulated as an end-to-end reconstruction task and neural networks are employed to directly estimate velocity models from seismic data. Most existing approaches rely on convolutional neural networks (CNNs) to extract local features from input data. However, compared with natural images, seismic data exhibit strong long-range dependencies that CNNs struggle to model effectively, since information associated with a single reflection interface is spatially distributed across the seismic data. Although CNNs perform well in computer vision, their local feature extraction mechanism limits reconstruction performance in FWI. To address this issue, we have proposed a novel FWI method based on the vision transformer (ViT). Unlike CNN-based approaches, the proposed method leverages the self-attention mechanism to model long-range dependencies in the input data. By stacking multiple transformer blocks, global physical information in seismic data is effectively encoded into the inversion process, resulting in improved reconstruction performance. Numerical results demonstrated that the proposed method outperforms CNN-based approaches in terms of reconstruction accuracy.

In practice, network operators tend to choose sparse communication topologies to cut costs, and the concurrent use of a communication network by multiple users commonly results in feedback delays. Our goal was to obtain the optimal sparse feedback control matrix $K$. For this, we proposed a sparse optimal control (SOC) problem governed by the cyber-physical system with varying delay, to minimize $||K|{|}_0$ subject to a maximum allowable compromise in system cost. A penalty method was utilized to transform the SOC problem into a form that was constrained solely by box constraints. A smoothing technique was used to approximate the nonsmooth element in the resulting problem, and an analysis of the errors introduced by this technique was subsequently conducted. The gradients of the objective function concerning the feedback control matrix were obtained by solving the state system and a variational system simultaneously forward in time. An optimization algorithm was devised to tackle the resulting problem, building on the piecewise quadratic approximation. Finally, we have presented of simulations.

This paper extends a result of isolated calmness for nuclear norm regularized convex optimization problems to Ky Fan $k$-norm regularized convex optimization problems. We find that there exists a certain equivalence relationship among the critical cones of the Ky Fan $k$-norm function and its conjugate as well as the "sigma term", namely, the conjugate function of the parabolic second-order directional derivative of the Ky Fan $k$-norm. By establishing the equivalence between the primal (dual) strict Robinson constraint qualification (SRCQ) and the dual (primal) second-order sufficient condition (SOSC), we derive a series of complete characterizations of the robust isolated calmness of the Karush-Kuhn-Tucker (KKT) mapping for Ky Fan $k$-norm regularized convex matrix optimization problems. The obtained results enrich the stability theory of the Ky Fan $k$-norm regularized convex optimization problems and further enhance the usability of the related algorithms.